Dynamic reconstruction multi-machine cooperative control method supporting digital twinning

Abstract

A dynamic reconstruction multi-machine cooperative control method supporting digital twinning is characterized by comprising the following steps: firstly, packaging a protocol and an interface of equipment, and constructing a data acquisition control module; secondly, establishing a dynamic reconstruction module comprising four libraries; thirdly, performing task dynamic allocation driven by quality, safety and efficiency based on the data acquisition control module and the dynamic reconstruction module; and fourthly, before the allocated task is executed, the system performs on-line detection or off-line correction on the occupied position and the position of the machined part to ensure the accuracy and consistency of task execution. By packaging the data acquisition control module and establishing the dynamic reconstruction module, dynamic reconstruction and registration of equipment and offline programming of analysis tasks are realized, dynamic and efficient distribution of processing tasks is realized, and improvement of processing precision and efficiency is ensured.

Description

Technical Field

[0001] The present invention relates to a multi-machine collaborative control method that supports dynamic reconstruction of digital twins, and in particular to the realization of dynamic digital twin simulation collaborative control for different processing workshop scenes, multiple processing equipment in the processing process, and non-unique workpieces. It can be used to solve the problem of dynamic reconstruction of scenes, equipment, and workpieces in simulation or twin environments. Background Art

[0002] With the rapid development of manufacturing industry towards intelligence and digitalization, digital twin technology has gradually become one of the important means to realize intelligent manufacturing. Digital twin technology realizes the synchronous mapping of virtual and real worlds by building virtual models of physical equipment and systems, so as to monitor, simulate and optimize various changes in the production process in real time.

[0003] In traditional manufacturing systems, equipment usually operates in isolation and cannot achieve real-time collaborative work, resulting in low resource utilization and low production efficiency. The introduction of digital twin technology makes collaborative control between manufacturing equipment possible. Through virtual simulation and real-time data interaction, different equipment can be rehearsed, optimized and actually operated in virtual space, thereby improving overall production efficiency.

[0004] In modern manufacturing environments, production tasks often require the coordinated work of multiple processing equipment, and the changes in different workshop scenes, processing technologies, and workpiece types require the manufacturing system to have a high degree of flexibility and adaptability. Traditional multi-machine collaborative control methods are usually based on static preset models, which are difficult to cope with dynamically changing production needs and complex on-site environments. The limitations of this method are mainly reflected in the following aspects:

[0005] First, the response speed is insufficient. Traditional control systems are slow to react to changes in the production site and are unable to adjust the coordination strategy between equipment in a timely manner, resulting in low production efficiency.

[0006] Secondly, there is a lack of dynamic reconstruction capabilities. In the actual production process, equipment configuration, workpiece status and processing requirements may change frequently, and traditional control methods are difficult to adjust and optimize in real time.

[0007] Finally, the collaboration is highly complex. The collaborative work between multiple devices requires precise synchronization and efficient communication. Traditional methods are prone to improper coordination or waste of resources when dealing with complex scenarios.

[0008] Existing multi-machine collaborative control methods are usually based on preset static models and are difficult to adapt to the dynamic changes that often occur in the manufacturing process. For example, different processing workshop scenarios, workpiece types, and changes in processing equipment configurations often require the system to be able to dynamically adjust and optimize during operation. When faced with these complex and changing production environments, traditional methods usually exhibit problems such as delayed response and lack of flexibility, making it difficult to meet the needs of modern intelligent manufacturing.

[0009] At present, most digital twin simulation collaborative control systems on the market are usually developed for a single scenario. These systems are usually designed for fixed processing equipment and specific processing workpieces, and their structures and functions are determined during development. Therefore, these systems are often fixed and single in applicability, and it is difficult to flexibly respond to changing needs in the production process. For example, when the equipment configuration or workpiece type in the production workshop changes, the existing simulation system often cannot adapt quickly, resulting in unsatisfactory simulation results.

[0010] In addition, these single-scenario digital twin simulation systems show obvious problems of low control efficiency and delayed response when facing multi-device and multi-machine collaborative operations. Since the system was not originally designed for multi-machine collaborative operations, its control algorithm and system architecture are difficult to operate effectively in complex collaborative scenarios. Coordination between devices is often delayed, making it difficult to ensure the synchronization and continuity of the entire production process, which in turn affects production efficiency and product quality.

[0011] These problems are particularly prominent in complex manufacturing environments. In modern manufacturing environments, the diversity and complexity of production tasks are increasing, and the frequent changes in different process flows, workpiece states, and equipment configurations require simulation systems to have higher flexibility and adaptability. However, due to its design limitations, existing digital twin simulation systems are unable to cope with these dynamic changes and cannot provide real-time, multi-scenario simulation support.

[0012] Based on the above problems, a new solution is urgently needed to improve the flexibility, adaptability and response speed of digital twin simulation collaborative control systems. Summary of the invention

[0013] The purpose of this invention is to address the deficiencies of traditional simulation systems in terms of adaptability, flexibility and response speed, and propose a dynamic reconstruction scheme and multi-machine collaborative control method for virtual simulation or digital twin environments. It aims to solve the challenges currently faced in the dynamic display of simulation and multi-machine collaborative control of multiple scenes, multiple devices and multiple workpieces.

[0014] The technical solution of the present invention is:

[0015] A dynamic reconstruction multi-machine collaborative control method supporting digital twins is characterized by:

[0016] First, the device protocols and interfaces are encapsulated to build a data acquisition control module. This encapsulation enables modular management of the lower-level device structure, allowing the upper-level control system to flexibly and uniformly call these device resources. Through this standardized design, the integrated control system can dynamically register any number of sensor modules to form a highly scalable and adaptable collaborative control system that supports dynamic reconstruction of multiple scenarios and flexible scheduling of multiple devices.

[0017] Secondly, a dynamic reconstruction module is established, which includes four main libraries: workshop library (production line library), product library (for storing related products or parts), equipment library (including automation equipment and tooling tools) and personnel library. These libraries are designed to support the dynamic reconstruction of the system. Each library mainly stores digital models, parameter models and corresponding databases related to its corresponding objects. For example, the workshop library stores digital models and configuration parameters of different production lines, and the product library stores detailed model information of various products and parts. Through this classified storage and management, when performing dynamic registration, the system can quickly find the required model data and load it into the digital twin simulation or control environment to provide support for subsequent dynamic reconstruction.

[0018] Third, based on the above-mentioned data acquisition control module and dynamic reconstruction module, the system can realize dynamic task allocation driven by quality, safety and efficiency. Specifically, in the task planning stage, the system will first comprehensively plan and simulate the tasks through offline programming software, and generate corresponding multi-station tasks. These tasks will then obtain the status information of each robot unit through the centralized control system, and then dynamically allocate the tasks to the most suitable robot unit for execution based on real-time data. This dynamic task allocation mechanism ensures that the workload of each device is optimized in a multi-device collaborative working environment. At the same time, when multiple devices are working at the same time, a coarse / fine bounding box dynamic algorithm is adopted to improve the overall production efficiency and quality control accuracy.

[0019] Fourth, before executing the assigned task, the system will first perform online detection or offline correction of the position of the placeholder and the position of the processed parts to ensure the accuracy and consistency of the task execution. If the system is offline, it will first perform offline simulation to simulate and verify every detail of the task. After the simulation is completed, the system will send the generated output results to the device for actual operation. If the system is online, the position of the processed parts will be accurately matched through online detection, and then the processing trajectory will be corrected online in real time. During the processing process, due to the large number of running devices, coarse and fine bounding boxes are added based on the device model. The coarse bounding box is used for range detection. When an object is detected within the range, the coarse bounding box is converted into a fine bounding box for anti-collision detection. If the fine bounding box detects an object, it is necessary to stop the current movement and adjust the state to ensure the operation safety, processing accuracy and consistency of product quality in the process of multi-device collaboration.

[0020] It can be seen that the present invention can realize dynamic reconstruction and collaborative control of multiple scenes, multiple devices, and multiple workpieces, effectively overcome the shortcomings of existing systems, and meet the simulation needs in complex production environments.

[0021] The beneficial effects of the present invention are:

[0022] The present invention solves the challenges faced in the dynamic display of simulation and multi-machine collaborative control of multiple scenes, multiple devices, and multiple workpieces. In view of the shortcomings of traditional simulation systems in terms of adaptability, flexibility, and response speed, a dynamic reconstruction scheme and multi-machine collaborative control method for virtual simulation or digital twin environments are proposed. This method can encapsulate the data acquisition control module, establish a dynamic reconstruction module, realize dynamic reconstruction and registration of equipment, offline programming and parsing tasks, realize dynamic and efficient allocation of processing tasks, and correct the trajectory before executing the task to ensure processing accuracy. This method has the following significant advantages:

[0023] First, the method forms a highly flexible control system by encapsulating device protocols and interfaces and modularizing various types of sensors. The system can dynamically register and schedule different device modules, allowing the system to quickly adapt to various changes on the production line, including changes in device configuration, workpiece types, and production tasks. This flexibility significantly improves the adaptability of the manufacturing system and enables it to cope with complex and changing production environments.

[0024] Second, the proposed dynamic reconstruction module and data acquisition control module are combined to enable the system to efficiently manage and schedule multiple devices. Through the collection and analysis of real-time data, the system can dynamically allocate tasks based on the status information of each device, ensuring that each device can work together in the optimal state. This not only improves the efficiency of multi-machine collaborative operation, but also reduces resource waste and production delays caused by improper equipment scheduling.

[0025] Third, this method dynamically adjusts the processing trajectory through online detection and offline correction before the task is executed. Whether the system is online or offline, it can ensure the processing accuracy through precise simulation and detection methods. In particular, the online detection and matching function can correct the processing trajectory in real time, ensure the consistency and high precision of product quality, and greatly reduce the error rate in the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 Overall technical solution for reconstruction of the automatic grinding and spraying control system of the whole machine.

[0027] Figure 2 Architecture of dynamic reconfiguration method for multi-robot integrated control systems.

[0028] Figure 3 Dynamic and efficient task allocation method for multi-machine collaboration.

[0029] Figure 4 Technical approaches to multi-robot collaboration and task allocation methods.

[0030] Figure 5 A holistic approach to dynamically reconfigure multi-machine collaborative control. DETAILED DESCRIPTION

[0031] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0032] like Figure 1-5 shown.

[0033] A dynamic reconstruction multi-machine collaborative control method supporting digital twins. The overall process is as follows Figure 5 shown.

[0034] Below Figure 1 The method of the present invention is described by taking the aircraft whole machine grinding and spraying system as an example, which specifically comprises the following steps:

[0035] The first step is to encapsulate the equipment's protocols and interfaces and build a data acquisition control module. The interfaces include data acquisition interfaces for multi-source sensors and processing equipment control interfaces; correspondingly, the interface transmission protocols mainly include interface transmission protocols for non-real-time information networks and real-time control networks. The interfaces and protocols are defined as standard unit modules, thereby supporting the integrated control system to dynamically register any number of standard modules, realize multi-machine collaborative motion control architecture, and support the construction, reorganization, expansion and maintenance of the whole machine grinding and spraying system for tasks of different sizes and complexities.

[0036] Take the grinding and spraying of the entire aircraft as an example:

[0037] (1.1) Define robots, AGVs, end effectors (grinding and spraying), and various sensors as standard unit modules;

[0038] (1.2) Encapsulate TCP / IP, OPC UA, EtherCAT, IO-Link and other protocols;

[0039] (1.3) During dynamic registration, a connection is quickly established with device sensors through encapsulated protocols to read or transmit data and complete reconstruction and simulation.

[0040] Step 2, such as Figure 2 As shown, a dynamic reconfiguration module is established, including a workshop library F = {f0, f1, ... f n-1}(Production Line Library), Product Library P = {P0, P1, ... P m-1} (store corresponding products or parts), equipment library D = {D0, D1, ... D q-1}(including automation equipment and tooling), personnel database S={S0,S1,…S j-1},The workshop library is selected during initialization or specified by the configuration file. The workshop library is the basis of the reconstructed control system and is the overall spatial description of a specific production line or workstation. Other products and equipment are placed within the scope of the workshop.

[0041] The product library stores parts and components, and one production line (workshop) may support the grinding and spraying of multiple products. The equipment library is used to store the equipment that may be used in each production line, including digital models, kinematic pairs, DH models and other information. When performing dynamic registration, the system can quickly find the required model data, load it into the simulation or control environment, and quickly complete the connection movement and information data communication of the equipment through the packaged interface and protocol. Through the pre-packaged model kinematic pairs and DH information, the real-time movement display of the equipment in the simulation environment is realized.

[0042] For the personnel library, its various character models and their joint motion pairs are pre-packaged. Each character model contains a SQLite data table, which contains the position information of all joints of the character model. When the scene detects that a person has entered, the character model is loaded; through visual detection, the recognized character joint information is written into the SQLite data table, and the character model completes the implementation of the person's simulation action through the data table information.

[0043] Step 3: Based on the data acquisition control module and the dynamic reconstruction module, the processing tasks are dynamically and efficiently allocated. Figure 3 As shown in the figure, the overall global task R is imported into the offline programming system or the global task R is written through the offline programming system. The global task R is decomposed into R = {r0, r1, ... r l-1}, where r i For a certain process in the overall task, further process r i Divide the tasks, i ={T i,0 ,T i,1 ,…T i,j-1}where i is the process number, j is the number of tasks under the process, T i,0 Not only the task content, but also the task status

[0044]

[0045] Then the global task R can be expressed as:

[0046]

[0047] The task status Rs can be expressed as:

[0048]

[0049] The device status is represented by:

[0050] DS={Ds0,Ds1,…Ds q-1} (4)

[0051] in:

[0052]

[0053] like Figure 4 As shown, its task allocation is R (i,) →D,R (i,) represents the task of the i-th step process; when the number of tasks is greater than |D|, task R i,j In its corresponding placeholder device D q When multiple occupants can perform tasks in the system, they are queued at the same time. The task status is read before reading the task. Only when the task Ts i,j = -1, the device performs the operation on the task; Rs (i,) =0 means all tasks of this process are completed and the next process R is carried out (i+1,) Task. Each AGV only accepts one processing task at a time, and its constraints are:

[0054] S i-1,q +P i-1,q i,q (6)

[0055] Among them, S i,q represents the start processing time of the ith task of the qth AGV, P i,q ​It represents the processing time of the i-th task of the q-th AGV; the AGVs at two adjacent stations cannot perform processing tasks at the same time to avoid interference during processing that affects processing quality and safety, and the constraints are:

[0056] Ds i-1 +Ds i+1 <1 (7)

[0057] In multi-AGV batch task allocation, task waiting time refers to the time difference between task release and actual execution. Since each AGV may accept multiple tasks at a time, the length of the task queue will directly affect the waiting time of the tasks in the queue. The total task waiting time T is a key comprehensive indicator that directly affects the system task execution efficiency and overall scheduling effect. Its calculation formula is as follows:

[0058]

[0059]

[0060]

[0061]

[0062] T is the total task waiting time; w is the task queue position number; For the kth task r k The task waiting time is calculated by the Hammond distance. As the starting point of the task, The end point of the mission, is the initial position of the device.

[0063] During the task allocation process, if some AGVs are busy for a long time while other AGVs are idle, the workload will be extremely unevenly distributed among the AGVs. This situation will not only reduce the overall efficiency of the system, but may also lead to overuse and loss of some AGVs. In order to avoid this problem, the load balancing index f2 is introduced. Its purpose is to maximize the balance of the workload of each AGV during task allocation, ensure that the task allocation of each AGV is more reasonable, and thus improve the stability and efficiency of the system. Its calculation formula is as follows:

[0064]

[0065]

[0066]

[0067]

[0068] eunload With e load is the energy consumption of the equipment when it is idle and during the processing; the value of cv is between [0,1], and the closer its value is to 0, the more balanced the equipment workload is.

[0069] This dynamic task allocation mechanism ensures that the workload of each device is optimized in a multi-device collaborative working environment, thereby improving overall production efficiency and quality control accuracy.

[0070] Step 4: perform trajectory detection and correction. The equipment obtains the assigned task and enters the corresponding station. Each measurement system detects the positioning posture of the station and determines the deviation between the actual position P′={p′1, p′2, p′3} and the theoretical position P={p1, p2, p3}. The system adopts online or offline compensation to correct it:

[0071] (4.1) If the offline correction method is adopted, the offline programming software obtains the measured posture, corrects the position of the points of the offline program instructions, and chooses whether to perform offline simulation verification as needed, and then sends the corrected position task instructions to the equipment for execution.

[0072] (4.2) If the online correction method is adopted, the master control system of the unit equipment will make position corrections to the points of the offline task instructions according to the obtained measured posture, and then send the corrected position task instructions to the equipment for execution (the online correction method can achieve the greatest degree of automation).

[0073] After the device completes the execution of the current instruction, the system proceeds to the next step based on the completion status of the entire station task list. If all tasks are completed, the process task is completed. Otherwise, the device waits for the system to dynamically assign the next task.

[0074] Step 5: In the multi-device processing process, based on the equipment model, the corresponding equipment adjacent kinematic pairs are used as nodes to add fine bounding boxes. The fine bounding boxes are generated based on the adjacent two kinematic pairs, with the starting joint as the center of motion, and include the connecting objects between the two joints; the coarse bounding boxes are generated based on the equipment working range and the size of the AGV, covering its working range and model size. During the motion process, the task tree method is used to achieve synchronous depth-first traversal of the two bounding box trees, which is conducive to improving the execution efficiency of the algorithm. Realize the safety guarantee of multi-machine collaborative work.

[0075] Take the grinding robot arm as an example:

[0076] (5.1) Get the joints of the robot model i and range of motion;

[0077] (5.2) In the joint iwith joint i+1 A rectangular bounding box is added between the connecting rods to envelop the connecting rods, and a fine bounding box is added. The rectangular bounding box of the AGV where the robot arm is located and the circular bounding box formed by the working range form a coarse bounding box.

[0078] (5.3) First, open the coarse bounding box and close the fine bounding box. During the movement, the task tree method is used. When the coarse bounding box detects that an object has entered, the coarse bounding box is closed and the fine bounding box is opened. When the fine bounding box detects that an object has entered, the task execution of the robot arm is stopped.

[0079] This patented method can also be applied to training scenarios of various types of equipment. By dynamically reconstructing different types and quantities of equipment, as well as the personnel involved, a specific virtual training environment can be formed, so that the corresponding training can be completed in a virtual simulation or digital twin system. For example, in a specific training scenario for lunar rover driving on the lunar surface, if astronauts need to drive different types of lunar rovers, operate multiple astronauts, and cooperate with each other, this dynamic reconstruction collaborative control method can be used to achieve training in a digital twin environment.

[0080] The present invention does not involve parts that are the same as the prior art or can be implemented by using the prior art.

Claims

1. A dynamic reconstruction multi-machine collaborative control method supporting digital twins, characterized by: First, the protocols and interfaces of the equipment are encapsulated to build a data acquisition control module. Through this encapsulation, modular management of the lower-level equipment structure is achieved, allowing the upper-level control system to flexibly and uniformly call these equipment resources. Through standardized design, the integrated control system can dynamically register the required sensor modules to form a highly scalable and adaptable collaborative control system that supports dynamic reconstruction of multiple scenarios and flexible scheduling of multiple devices. Secondly, a dynamic reconstruction module is established, which includes four libraries: a workshop library, i.e., a production line library, a product library for storing related products or parts, an equipment library containing automation equipment and tooling tools, and a personnel library. These libraries are designed to support the dynamic reconstruction of the system. Each library stores digital models, parameter models, and corresponding databases related to its corresponding objects. Through classified storage and management, the system can quickly find the required model data during dynamic registration and load it into the simulation or control environment to provide support for subsequent dynamic reconstruction. Third, based on the above data acquisition control module and dynamic reconstruction module, dynamic allocation of tasks driven by quality, safety and efficiency is performed; Fourth, before executing the assigned task, the system will first perform online detection or offline correction of the position of the occupied position and the position of the processed parts to ensure the accuracy and consistency of task execution; If the system is offline, it will first perform offline simulation to simulate and verify every detail of the task; after the simulation is completed, the system will send the generated output results to the device for actual operation; if the system is online, it will accurately match the position of the processed parts through online detection, and then perform real-time online corrections to the processing trajectory; in the processing process, due to the large number of running devices, coarse and fine bounding boxes are added based on the device model. The coarse bounding box is used for range detection. When an object is detected within the range, the coarse bounding box is converted into a fine bounding box for anti-collision detection. If the fine bounding box detects an object, it is necessary to stop the current movement and adjust the state to ensure the operation safety, processing accuracy and product quality consistency during the collaboration of multiple devices.

2. The method according to claim 1, characterized in that: The workshop library stores the digital models and configuration parameters of different production lines, while the product library stores the detailed model information of various products and parts.

3. The method according to claim 1, characterized in that: During dynamic allocation, in the task planning stage, the system will first use offline programming software to comprehensively plan and simulate the task and generate corresponding multi-station tasks; These tasks will then obtain the status information of each robot unit through the centralized control system, and then dynamically assign the tasks to the most suitable robot unit for execution based on real-time data; through the dynamic task allocation mechanism, it is ensured that in a multi-device collaborative working environment, the workload of each device is optimized. At the same time, when multiple devices are working at the same time, the coarse / fine bounding box dynamic algorithm is used to improve the overall production efficiency and quality control accuracy.

4. The method according to claim 1 or 3, characterized in that: When performing dynamic allocation, first import the overall global task R into the offline programming system or write the global task R through the offline programming system, and decompose the global task R into R = {r0, r1, ...r l-1 }, where r i For a certain process in the overall task, further process r i Divide the tasks, i ={T i，0 , T i，1 , ...T i，j-1 }where i is the process number, j is the number of tasks under the process, T i，0 It contains not only the task content, but also the task status: Then the global task R can be expressed as: The task status Rs can be expressed as: The device status is represented by: DS={Ds0,Ds1,...Ds q-1 } (4) in: The task is assigned to R (i，) →D, R (i，) represents the task of the i-th step process; when the number of tasks is greater than |D|, task R i，j In its corresponding placeholder device D q When multiple occupants can perform tasks in the system, they are queued at the same time. The task status is read before reading the task. Only when the task Ts i,j = -1, the device performs the operation on the task; Rs (i，) = 0 means that all tasks of this process are completed and the next process R(i +1， ) task; each AGV only accepts one processing task at a time, and its constraints are: S i-1，q +P i-1，q <S i,q (6) Among them, S i,q represents the start processing time of the ith task of the qth AGV, P i，q It represents the processing time of the i-th task of the q-th AGV; the AGVs at two adjacent stations cannot perform processing tasks at the same time to avoid interference during processing that affects processing quality and safety, and the constraints are: Ds i-1 +Ds i+1 <1 (7) In multi-AGV batch task allocation, task waiting time refers to the time difference between task release and actual execution. Since each AGV may accept multiple tasks at a time, the length of the task queue will directly affect the waiting time of the tasks in the queue. The total task waiting time T is a key comprehensive indicator that directly affects the system task execution efficiency and overall scheduling effect. Its calculation formula is as follows: T is the total task waiting time; w is the task queue position number; For the kth task r k The task waiting time is calculated by the Hammond distance. As the starting point of the task, The end point of the mission, is the initial position of the device; In the process of task allocation, if some AGVs are busy for a long time while other AGVs are idle, the workload will be extremely unevenly distributed among the AGVs. This situation will not only reduce the overall efficiency of the system, but also lead to excessive use and loss of some AGVs. For this reason, the load balancing index f2 is introduced to maximize the balance of the workload of each AGV during task allocation, ensure that the task allocation of each AGV is more reasonable, and thus improve the stability and efficiency of the system. Its calculation formula is as follows: e unload With e load is the energy consumption of the equipment when it is idle and during the processing; the value of cv is between [0,1], and the closer its value is to 0, the more balanced the equipment workload is.

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